Image fragmentation for distortion correction of color space encoded image

ABSTRACT

Embodiments of the present disclosure can include devices for storing and exchanging color space encoded images. The encoded images can store input data into high capacity multi-colored composite two-dimensional pictures having different symbols organized in specific order using sets in a color space. The encoding can include performing two-level error correction and generating frames based on the color space for formatting and calibrating the encoded images during decoding. The decoding can use the frames to perform color restoration and distortion correction. The decoding can be based on a pseudo-Euclidean distance between a distorted color and a color in a color calibration cells. In some embodiments, an encoded image can be further divided into sub-images during encoding for simplified distortion correction.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is related to the following applications:

U.S. patent application Ser. No. 13/836,447, filed concurrently herewithand entitled “Data Storage and Exchange Device For Color Space EncodedImages;”

U.S. patent application Ser. No. 13/842,932, filed concurrently herewithand entitled “Broadcasting Independent of Network Availability UsingColor Space Encoded Image;”

U.S. patent application Ser. No. 13/837,155, filed concurrently herewithand entitled “Image Encoding and Decoding Using Color Space;”

U.S. patent application Ser. No. 13/837,895, filed concurrently herewithand entitled “Color Restoration for Color Space Encoded Image;”

U.S. patent application Ser. No. 13/842,856, filed concurrently herewithand entitled “Two-Level Error Correcting Codes for Color Space EncodedImage;”

U.S. patent application Ser. No. 13/843,111, filed concurrently herewithand entitled “Frame of Color Space Encoded Image for DistortionCorrection;”

U.S. patent application Ser. No. 13/842,817, filed concurrently herewithand entitled “Information Exchange Using Photo Camera as Display forColor Space Encoded Image;”

U.S. patent application Ser. No. 13/843,132, filed concurrently herewithand entitled “Information Exchange Display Using Color Space EncodedImage;”

U.S. patent application Ser. No. 13/841,338, filed concurrently herewithand entitled “Information Exchange Using Color Space Encoded Image;”

U.S. patent application Ser. No. 13/844,184, filed concurrently herewithand entitled “Large Documents Using Color Space Encoded Image;”

U.S. patent application Ser. No. 13/844,207, filed concurrently herewithand entitled “Combination Book With E-Book Using Color Space EncodedImage;”

U.S. patent application Ser. No. 13/844,000, filed concurrently herewithand entitled “Book Using Color Space Encoded Image;”

U.S. patent application Ser. No. 13/844,127, filed concurrently herewithand entitled “Data Backup Using Color Space Encoded Image;” and

U.S. patent application Ser. No. 13/844,168, filed concurrently herewithand entitled “Self-Publication Using Color Space Encoded Image;” and

U.S. patent application Ser. No. 13/840,504, filed concurrently herewithand entitled “Information Broadcast Using Color Space Encoded Image.”

FIELD OF THE DISCLOSURE

The present disclosure relates to a data storage and exchange device forcolor space encoded images, and methods of encoding and decoding colorspace encoded images. More particularly, the present disclosure relatesto encoding and decoding of machine-based data using high capacitymulti-colored composite two-dimensional pictures having differentsymbols organized in specific order and sets in a color space.

BACKGROUND

With smartphones and tablets having become widely deployed as well as 3Gand Wi-Fi based access to Internet, data capture technologies work as aninterface between databases and user devices. Example data capturetechnologies include bar codes, QR codes, Radio Frequency Identification(RFID) and Near Field Communication (NFC). Most of these technologiesare used as either a visible or an invisible tag to connect thesedatabases and user devices. The introduction of cloud storage addsextended use of data exchange between data storage and end user devices.

Many households include many different devices simultaneously connectedto Wi-Fi access points. This ubiquitous Wi-Fi usage increaseselectromagnetic fields around the home environment. Despite theusefulness of Wi-Fi, long term consequences for the human body andgeneral health are not clear.

End user devices such as smartphones and tablets are frequently equippedwith photo and/or video cameras. These cameras can be used for capturinginformation presented in different visual forms. The other data capturetechnologies descried earlier, including bar codes and especially twodimensional (2D) bar codes such as QR codes, have attracted theattention of many researchers and companies due to the potential forinexpensive operation.

Bar codes have been used for mobile applications to deliver a multitudeof different mobile services over mobile phones and other mobilecommunication or computing devices. Such applications range fromproviding Uniform Resource Locator (URL) information to link a mobiledevice to the Internet, through to using a bar code as a form ofe-ticket for airlines or event admissions. Hence, there is an evergrowing demand for higher capacity bar codes, suited for robustapplication on mobile devices. Traditional approaches to higher capacitybar codes include: (i) using a colored symbol set and (ii) increasingthe size of the bar code. There are known limitations for eitherapproach, especially related to mobile devices with compromised, lowresolution cameras. The traditional data capacity of 1D and 2D bar codesis severely limited. This severely limited data capacity constrainspossible applications of 1D and 2D bar codes, and their primary task issimply linking camera phones to designated Web sites. Additional tasksmay then be performed based on the Web site. This operation again isbased on using of Wi-Fi or 3G connectivity.

The maximum data capacity of 2D bar codes has been improving over therecent years, resulting in the introduction of newer applications.

Presently, the use of color-based bar code and other symbolic encodingin color space technologies using mobile devices such as camera mobilephones has been limited by the physical size of the actual bar codesymbol in relation to the information encoded within, and also by theimage capturing mechanism on mobile devices to discriminate or resolveeffectively a greater multitude of colors, especially under variedlighting conditions by an inexperienced, lay user. Another limitingfactor has been color fidelity of hard copy output devices, such ascolor printers, in reproducing the true colors of such color based barcode or symbol sets in a color space.

While bar codes can be used to provide 2D encoded data, they have notbeen used to provide real storage devices and media.

SUMMARY

Embodiments of the present disclosure can include devices for storingand exchanging color space encoded images. The encoded images can storeinput data into high capacity multi-colored composite two-dimensionalpictures having different symbols organized in specific order using setsin a color space. The encoding can include performing two-level errorcorrection and generating frames based on the color space for formattingand calibrating the encoded images during decoding. The decoding can usethe frames to perform color restoration and distortion correction. Thedecoding can be based on a pseudo-Euclidean distance between a distortedcolor and a color in color calibration cells. In some embodiments, anencoded image can be further divided into sub-images during encoding forsimplified distortion correction.

In accordance with the disclosed subject matter, methods and systems areprovided for encoding data using a color space and decoding an imageusing a color space.

Certain embodiments include methods of encoding data using a colorspace. The method includes receiving data for encoding and determining acode sequence by performing error correction on the data. The methodalso includes creating colored cells to provide an encodedrepresentation of the received data in an image by determining a colorencoding for one or more codes in the code sequence by mapping the codesequence to a stream of integers. Each integer in the stream of integerscorresponds to a color in the color space for a cell in the image. Themethod further includes partitioning the colored cells into subimages ofthe image, the subimages providing smaller areas for assisting indecoding the image and creating one or more additional cells adjoiningthe image, the one or more additional cells for assisting in decodingthe image.

Certain embodiments include methods of decoding an image using a colorspace. The method includes receiving an image to decode, locating theimage by locating one or more additional cells adjoining the image, andlocating one or more subimages of the image. The method also includes,for each subimage, decoding data based at least in part on colored cellsin the subimage and the one or more additional cells adjoining theimage.

Certain embodiments include systems for encoding data using a colorspace. The system includes one or more displays and storage. The systemalso includes at least one processor configured to receive data forencoding, determine a code sequence by performing error correction onthe data, and create colored cells to provide an encoded representationof the received data in an image by determining a color encoding for oneor more codes in the code sequence by mapping the code sequence to astream of integers. Each integer in the stream of integers correspondsto a color in the color space for a cell in the image. The at least oneprocessor is further configured to partition the colored cells intosubimages of the image, the subimages providing smaller areas forassisting in decoding the image and create one or more additional cellsadjoining the image, the one or more additional cells for assisting indecoding the image.

Certain embodiments include systems for decoding an image using a colorspace. The system includes one or more displays and storage. The systemalso includes at least one processor configured to receive an image todecode, locate the image by locating one or more additional cellsadjoining the image, and locate one or more subimages of the image. Foreach subimage, the at least one processor is further configured todecode data based at least in part on colored cells in the subimage andthe one or more additional cells adjoining the image.

The embodiments described herein can include additional aspects of thepresent invention. For example, the additional cells can include a framesurrounding the image. The additional cells can include colorcalibration cells adjoining the image.

BRIEF DESCRIPTION OF THE DRAWINGS

Various objects, features, and advantages of the present disclosure canbe more fully appreciated with reference to the following detaileddescription when considered in connection with the following drawings,in which like reference numerals identify like elements. The followingdrawings are for the purpose of illustration only and are not intendedto be limiting of the invention, the scope of which is set forth in theclaims that follow.

FIG. 1 illustrates a system for storing, encoding, and decoding a 2Dcolor space image in accordance with certain embodiments of the presentdisclosure.

FIGS. 2A-2B illustrate the 2D color space encoded image in accordancewith certain embodiments of the present disclosure.

FIG. 3 illustrates a method that the system performs for encoding animage in accordance with certain embodiments of the present disclosure.

FIG. 4 illustrates a method that the system performs for decoding animage in accordance with certain embodiments of the present disclosure.

FIG. 5 illustrates a block diagram of color calibration cells forrestoration of proper color based on distinguishable color samples inaccordance with certain embodiments of the present disclosure.

FIG. 6 illustrates a block diagram of a frame arrangement forgeometrical distortion correction of an encoded image in accordance withcertain embodiments of the present disclosure.

FIG. 7 illustrates a block diagram for frame fragmentation fordistortion correction in accordance with certain embodiments of thepresent disclosure.

DETAILED DESCRIPTION

The present systems and methods allow for use of 2D color-space codes toencode images with greater data capacity. The 2D color-space encodedimages described herein can provide a wide variety of applications,regardless of network connectivity. The present systems and methodsallow a sufficient amount of data or even an entire database to beencoded and placed in a storage device using the 2D images encoded andorganized by color-space codes as described herein.

The present disclosure has utility in, for example, data back-up storageapplications or data exchange using mobile devices to provide colortwo-dimensional pictures for processing.

FIG. 1 illustrates a system 100 for storing, encoding, and decoding a 2Dcolor space image in accordance with certain embodiments of the presentdisclosure. System 100 includes a 2D color space image 102. System 100can encode any type of desired machine-based data. Non-limiting examplesof machine-based data include text 104 a, books 104 b (containing, forexample, text and images), databases 104 c, photographs 104 d or otherartwork, images, video, film, or movies, music 104 e, or any other typeof binary data 104 f.

In some embodiments, capturing of desired data can be done using aconventional camera or scanner, or a smartphone or tablet camera.

For example, a user can encode selected information into a 2Dcolor-space coded image and print the image on any desired media (e.g.,paper, textile, plastic, etc.). The 2D color-space coded image can alsobe depicted on any display, including posters, TV screens or computermonitors, as well as displayed on mobile devices such as smartphones ortablets. Digital photo cameras can be used as display devices as well.

Using a capturing device (e.g., a conventional camera or scanner or asmartphone or tablet camera) with an external PC or internal processorhaving image reading or decoding software, the image code can be decodedand the encoded data can be read. The present systems and methods allowinformation to be retrieved, decoded, and saved in the memory of adevice or presented on a display for further use. For example, thedecoded data can be read on a laptop computer 106 a or a desktopcomputer 106 b, a smart phone 106 c, a photo camera 106 d, a palmtop orhandheld device 106 e, or a tablet 106 f. All this can be done withoutmanual work. No network connectivity or additional cost is required.

High Volume Data Storage and Exchange Device and Encoding/DecodingMethods

The present systems and methods relate to a data storage device forstoring 2D color space encoded images, methods of encoding and decodingthe 2D color space encoded images, and application of the 2D color spaceencoded images to data exchange.

More particularly, the present disclosure relates to encoding,transmission, and decoding of machine-based data using an appropriatelyformatted high capacity color image. The present systems and methods canbe utilized, for example, for compact backup of printed materials,and/or data exchange between a color-image-displaying device and aplurality of image-reading devices.

FIGS. 2A-2B illustrate the 2D color space encoded image 102 inaccordance with certain embodiments of the present disclosure. Asillustrated in FIG. 2A, encoded image 102 includes a number ofrectangular cells 202 of different colors. In some embodiments, encodedimage 102 can include a black frame 204, a white frame 206, a “zebraframe” 208 having alternating colors, and color calibration cells 210.

FIG. 2B illustrates a close-up view of the black frame 204, the whiteframe 206, the zebra frame 208, and the color calibration cells 210 inaccordance with certain embodiments of the present disclosure. Blackframe 204 and white frame 206 can be used for rough localization of thedata image including rectangular cells 202, determination of geometricdistortion of the data image, and estimation of cell size and scale ofrectangular cells 202. Zebra frame 208 can be used for furthercalibration of cell size for rectangular cells 202. Color calibrationcells 210 can be used for determining a number of colors N_(c) used forencoding the data, and for indexing the colors to map the colors tonumbers for encoding and decoding.

In some embodiments, rectangular cells 202 can use more than eightdifferent colors. The number of colors can be expressed as N_(c), forexample N_(c)≧8. Preferably, the number of colors is selected to be apower of two.

FIG. 3 illustrates a method 300 that the system performs for encoding animage in accordance with certain embodiments of the present disclosure.

The choice of appropriate color-difference metrics can help achievestable color resolution for image-source media and image-readingdevices. Most imaging devices operating in the visible spectrum areadapted to human eye color resolution. A color space describes colorcapabilities of a particular device by relating numbers to actualcolors. Embodiments of the present disclosure utilize metrics based onlow-cost non-Euclidean distance in red green blue (RGB) color space.

In some embodiments, the present system uses color space metrics whichare close to Commission International de l′eclairage (CIE) 1976(L*,u*,v*) color space metrics. The CIELUV color space distributescolors roughly proportional to perceived color differences. In otherembodiments, the present systems and methods are able to use any colorspace, including the CIE 1931 XYZ color space, CIELAB color space, andCIEUVW color space. Although embodiments of the present system are basedon the CIELUV color space, according to experimentation, the color spaceused in certain embodiments is advantageously less sensitive to a colortemperature of relevant image displaying media or device, andadvantageously less sensitive to external illumination. Assuming R, G,and B are one byte (i.e., 0.255 or 0x0 . . . 0xFF) normalized intensityof red, green and blue color components correspondingly, a colordifference ΔC between colors C₁=(R₁, G₁, B₁) and C₂=(R₂, G₂, B₂) can begiven by the following equations:

$\begin{matrix}{\mspace{79mu}{{\overset{\_}{r} = \frac{R_{1} + R_{2}}{2}}\mspace{79mu}{{\Delta\; R} = {R_{2} - R_{1}}}\mspace{79mu}{{\Delta\; G} = {G_{2} - G_{1}}}\mspace{79mu}{{\Delta\; B} = {B_{2} - B_{1}}}{{\Delta\; C} = \sqrt{{\left( {2 + \frac{\overset{\_}{r}}{256}} \right)\Delta\; R^{2}} + {4\;\Delta\; G^{2}} + {\left( {2 + \frac{255 - \overset{\_}{r}}{256}} \right)\Delta\; B^{2}}}}}} & {{Equation}\mspace{14mu}(1)}\end{matrix}$where r describes an average red component value of C₁ and C₂.

The present system first receives machine data for encoding into animage. For example, the machine data can be contained in a file. Thepresent system compresses the file (step 302). For example, the machinedata can be compressed into a binary compressed file such as zip, rar,arc or any other archive format file.

The present system then determines an appropriate color encoding,illustrated by box 310. For example, the present system maps theresulting code sequence to a stream of integers from 0 to N_(c)−1,inclusive. Using the stream of integers, the present system creates abitmap with frame formatting. Generation of frame formatting isdescribed in further detail later. The present system fills the interiorof the frames with color bitmap cells, encoded as described following.In some embodiments, the encoding can use error correction. Anon-limiting example of error correction is the two-stage errorcorrection described herein. In some embodiments, the encoding can usescrambling. In some embodiments, the encoding can use error correctionin combination with scrambling. The present system determines the colorof each cell based on the color calibration cells. The resulting encodedimage can then be sent to any display, including a color printer,display, or other color image output device.

In some embodiments, the present system then performs a first level oferror correction (step 304). One benefit of error correction is that thepresent system is able to decode an image properly, even if a portion ofthe image is damaged. For example, the present system can determine aheader containing a cyclic redundancy check (CRC) error-detecting code,file name, format information, and sequence number (e.g., if the machinedata and resulting encoded image represent only part of a larger datastream such as a video stream). In some embodiments, the present systemcan use any error detecting code such as repetition codes, addition ofparity bits, checksums, hash functions, or more powerful errorcorrecting codes.

In some embodiments, the present system can use a Reed-Solomon encoderof high order. A Reed-Solomon error correction code is a cyclicerror-correcting code capable of detecting and correcting multipleerrors in a bit stream. For example, the present system can use 8-bitwords and blocks of size 255 bytes, if N_(c)=16. In further embodiments,the present system can use other error-correcting codes such aslow-density parity-check (LDPC) codes or turbo codes. Reed-Solomon errorcorrection codes are capable of detecting errors, erasures, andcombinations thereof. In some embodiments, a word size for this highorder level of Reed-Solomon encoding can be equal to k*log₂(N_(c))rounded down, where k>1. As described herein, in some embodiments thetwo-stage error coding can perform high order Reed-Solomon encoding in afirst stage, and low order Reed-Solomon encoding in a second stage. Thesecond stage can also include smaller words. For example, the word sizecan be equal to log₂(N_(c)) bits, rounded down.

In some embodiments, the present system then scrambles the obtained code(step 306). For example, the resulting code can be interleaved using apredefined pseudorandom sequence. The present system scrambles theobtained code to spread out potential errors. Accordingly, the two-levelerror correction described herein can perform better. That is, whenregistering or capturing an image for decoding, there can be colordistorgions due to non-uniform lighting, or damage on the media on whichthe image is printed. For example, the media can have spots, bewrinkled, and the like. The scrambling provides that “bad” color cellswith uncertain data can be distributed uniformly (i.e., independently)into different encoding blocks, thereby decreasing the number of errorsper block. Because error correction can have upper limits of correctableerrors, the present spreading improves the effectiveness of the errorcorrection.

In some embodiments, the present system then performs a second level oferror correction (step 308). For example, the resulting scrambled codeis encoded using a Reed-Solomon encoder with. For the second level oferror correction, the word size can be selected to be smaller than theword size used in the first level of error correction. As describedearlier, this two-stage error correction allows the present system tomark unrecoverable blocks of data during a first stage as erasures. Theerror correction described herein finds an index location of an errorword inside a block, and also corrects the error word to its correct ortrue value. The two-stage error correction allows the present system tocorrect twice as many erasures as errors, as described in more detaillater.

Registration

The present system is able to use any kind of high definition (HD) colorregistration device to register an encoded image. Non-limiting exampleregistration devices can include a digital camera, a smartphone camera,a tablet camera, a video camera, a webcam, a scanner (to scan a hardcopy image), etc.

Decoding

FIG. 4 illustrates a method 400 that the system performs for decoding animage in accordance with certain embodiments of the present disclosure.

Once an encoded image is registered, the present system can copy aregistered image to any processing device containing a decoding module.An example processing device can include a computer, containing adecoding module with computer-readable instructions for decoding, wherethe computer-readable instructions are stored on a non-transitorycomputer storage medium, or containing hardware having hard-coded orembedded decoding instructions stored thereon.

Generally, decoding method 400 includes two high-level processes: (1)image recognition, and (2) two-stage Reed-Solomon decoding. In someembodiments, the present system uses Viterbi decoding as a Reed-Solomondecoder.

The present system locates the main frame (step 402). For example, thepresent system can locate the black frame and the white frame vertices.The present system can further determine the middles of thecorresponding edges of the black frame and the white frame. If the mainframe is not found (step 404: No), the present system returns an error(step 406). For example, the error can be that the image cannot bedecoded, or the error can be more specific such as that the main framecannot be found.

If the main frame is found (step 404: Yes), the present system correctsgeometric distortion based on the main frame and the zebra frame (step412). Geometric distortion can occur when a user uses a camera lens, forexample on a smartphone or tablet camera, or on a digital camera, todecode an image. Accounting for distortion is described in furtherdetail later, in connection with FIG. 6.

The present system locates the color calibration cells (step 408). Asdescribed earlier in connection with FIGS. 2A-2B, in some embodiments,the color calibration cells are located inside the black frame and thewhite frame, on the left of the rectangular cells encoding the machinedata. In other embodiments, the color frame could be located outside theblack frame, between the black and white frames, or in other locationswithin the white frame.

Based on the color frame, the present system decodes the colors in therectangular cells (step 410). In general, for every pixel found in theencoded image, the present system finds the closest sample pixel basedon the color calibration cells and substitutes the sample pixel for thedetected pixel. In some embodiments, the present system obtains acolor-value correspondence. The present system determines cell sizecalibration based on the zebra frame. The present system divides thedata image into color cells. When decoding the image, the present systemcaptures or collects all colors belonging to a given pixel rectangle. Ifmost of the pixel colors in the pixel rectangle could be recognized asone color from the color calibration cells, that color from the colorcalibration cells is used. Otherwise, the present system marks the pixelas an erasure. If a pixel-level block cannot be decoded, the presentsystem treats the whole pixel block as an erasure. The present systemreads the rectangular cells sequentially and determines an index of thenearest color from the color calibration cells to the median cell color.In particular, the present system determines the nearest color accordingto nearest Euclidean distance in the appropriate color space, accordingto Equation (1), described earlier.

The present system decodes the bytes (step 414). In some embodiments,the present system applies a corresponding decoder to theerror-correcting encoder described earlier. For example, the presentsystem applies a low-level Reed-Solomon decoder to the code streamobtained from the decoded colors. Accordingly, due to the nature of theerror-correcting code, the present system is able to restore erasuresand errors in the code stream. If the present system detectsunrepairable blocks, the unrepairable blocks are marked as “erasures.”

The present system then descrambles the bytes (step 416). In someembodiments, the present system de-interleaves the resulting bytestream.

The present system then decodes the original information based on thedecoded byte stream (step 418). In general, the present system uses acorresponding decoder to the error-correcting encoder described earlier,to restore pixel-block-level erasures and errors. In some embodiments,the present system applies a high-level Reed-Solomon decoder. Afterapplying the high-level Reed-Solomon decoder, if some pixel blocksremain un-repairable, the present system attempts to change the indicesto those indices corresponding to the next nearest color in the colorcalibration cells, and then repeats Reed-Solomon decoding of themodified block. If error free data is obtained, the present system mapsthe resulting sequence of integers to a bit stream. The present systemthen parses the resulting bit stream and computes a cyclic redundancycheck (CRC) of the data. The present system compares the resulting CRCwith the CRC contained in the header of the image. The present systemthen saves or appends the file.

Two-Level COLOR Error Correction Schema with Erasures and StatisticalColor Restoration

The two-level error correction used in the present system and describedearlier in connection with FIGS. 3 and 4 allows the present system tocorrect two types of errors: errors and erasures. In some embodiments,the Reed-Solomon error-correction encoding used is able to detect errors(which correspond to wrong, incorrect, or unexpected information) anderasures (which correspond to absent or missing information).

In some embodiments, correcting erasures can be performed twice aseffectively than correcting errors. To make use of this feature, thepresent system uses a two-level error correction schema.

For example, assume N_(c)=16 (i.e., 2⁴). Accordingly, there are sixteendistinctive colors which allow an encoded image to encode 4 bits ofinformation per rectangular cell. Every byte of information (8 bits) isencoded by two color indices. The error correction scheme has twolevels. First, the byte array is divided into chunks, and every chunk isappended with Reed-Solomon source control data. Second, every byte isdivided into two half-bytes (4 bits). The half-bytes are then gatheredinto groups or chunks which are appended with Reed-Solomon sourcecontrol data. For example, the present system can process <N_(c)−1half-bytes. That is, if N_(c)=16, the present system gathers less thanfifteen half-bytes. The group of half-bytes is then appended withReed-Solomon source control data determined based on the group ofhalf-bytes. Because the Reed-Solomon source control data can be aboutthe same size as the input data, the addition of the Reed-Solomon sourcecontrol data results in the size of the group reaching fifteen bytes.For example, the size of the input data and the control data block sizecan be fifteen bytes. Each half-byte in the group is mapped to anappropriate color, for example by using the corresponding half-bytes asindices into the color array. Accordingly, the encoding process appliestwo levels of Reed-Solomon encoding, first at the byte array level andsecond at the half-byte level.

As described earlier, when decoding the image, the present systemcollects all colors belonging to a detected pixel rectangle. If most ofthe colors in the pixel rectangle could be recognized as one of thecolors in the color calibration cells, the present system uses thecorresponding color in the color calibration cells. Otherwise, the pixelrectangle is marked as an erasure. If a pixel-level block cannot bedecoded, the whole pixel block is treated as an erasure. As describedearlier, the erasures can be further restored by an appropriateerror-correction algorithm. The two-level error correction andleveraging of erasure determinations significantly improves the qualityof the decoding described earlier in connection with FIG. 4. Inparticular, the two-level error correction described herein allows thepresent system to recover information which otherwise would be lost.

Restoration of Proper Color in the Case of Distinguishable Color Samples

FIG. 5 illustrates a block diagram of color calibration cells forrestoration of proper color based on distinguishable color samples inaccordance with certain embodiments of the present disclosure. FIG. 5includes color calibration cells 210.

Color calibration cells 210 include all colors used to encode therectangular cells. In some embodiments, a white frame or bordersurrounds color calibration cells 210 for the color white. In otherembodiments, the present system may also include white as a color blockin the color calibration cells arranged on the left hand side. Thepresent system discerns each color based on its placement within colorcalibration cells 210. The present system then stores distorted pixelsas color samples. Different variants of a distorted pixel are saved topoint or refer to the same correct pixel. That is, distorted pixels aredetermined dynamically, based on the position of their colors among thecolor calibration cells. The distorted pixels are assigned estimatedcolors according to the color calibration cells.

As described earlier, during decoding (shown in FIG. 4), the presentsystem translates a distorted pixel into one of the original colors incolor calibration cells 210 using the nearest original color to thedistorted pixel. For example, let color C=(R, G, B) represent a redgreen blue (RGB) component representation of the color. According toEquation (1), described earlier, a pseudo-Euclidean distance between twocolors C₁ and C₂ can be given by ΔC, as described in Equation (1):

${\Delta\; C} = \sqrt{{\left( {2 + \frac{\overset{\_}{r}}{256}} \right)\Delta\; R^{2}} + {4\;\Delta\; G^{2}} + {\left( {2 + \frac{255 - \overset{\_}{r}}{256}} \right)\Delta\; B^{2}}}$

This distance generates a native pseudo-Euclidean metric in the colorspace. A color mapping can be represented by the set of pairs:(distorted color)→(correct color). The distorted color can be C₁, andthe correct color can be C₂. The number of such color pairs should begreater than or equal to N_(c), the total number of colors. This mappingcan be determined based on the color calibration cells and on the colorarrangement therein.

To restore a correct or proper color c* from a distorted color, thepresent system iterates over all pairs of distorted colors and correctcolors. In particular, the present system selects the pair in which thedistorted color portion of the pair is closest to the correct or propercolor c*. In some embodiments, the determination of whether thedistorted color portion is closest to the correct or proper color ismade according to the pseudo-Euclidian metrics in the RGB color space.That is, the present system selects the pair in which ΔC is smallestaccording to Equation (1). The selected pair provides the correct color.Accordingly, the present system allows the correct colors to berestored, because the present system saves the correspondence betweenthe distorted values and the original values. Therefore, the presentsystem is able to leverage information about the way in which the colorswere distorted relative to the original values as captured in the colorcalibration cells.

Frame Arrangement for Automatic Image Geometrical Distortion Correction

FIG. 6 illustrates a block diagram of a frame arrangement forgeometrical distortion correction of an encoded image in accordance withcertain embodiments of the present disclosure.

In some embodiments, the present system includes arrangements torecognize placement of rectangular cells and other elements in theencoded image. For example, these arrangements can include black frame204, white frame 206, zebra frame 208, and color calibration sidebar 210(shown in FIG. 2). The black frame and white frame surround the encodedrectangular cells. The color calibration sidebar contains sample colors(described earlier, in connection with FIG. 5). The zebra framesurrounds the encoded rectangular cells to allow the present system todetermine appropriate resolution and pixel division of the encodedimage.

The black frame and white frame allow the present system to determineand correct nonlinear distortion. As described earlier, nonlineardistortion can be introduced, for example, by camera lenses whenregistering an encoded image for decoding. In some embodiments, thepresent system uses the white frame as follows. The white frame can berestricted by curve x_(left)(y) 602, curve x_(right)(y) 604, curvey_(top)(x) 606, and curve y_(bottom)(x) 608. For every point (x*,y*) 610which belongs to the area restricted by curves 602-608, the presentsystem can determine parameters a and b (0≦a≦1 and 0≦b≦1) according tothe following equations:x _(left)(y*)+ax _(right)(y*)=x*  (1-a)y _(top)(x*)+by _(bottom)(x*)=y*  (1-b)The equation described above maps the area restricted by curves 602-608to a unit rectangle defined by 0≦a≦1, 0≦b≦1. The unit rectangle canrepresent what the image would look like when “straightened out,” bymapping the area restricted by curves 602-608 to the unit rectangle.That is, the distorted boundary of the image determined according to thearea restricted by curves 602-608 can be mapped to the “straightenedout” unit rectangle. The zebra frame can be used to determine parametersa and b. The present distortion correction therefore allows the originalarea having curved or non-linear sub-regions to be transformed accordingto the unit rectangle using parameters a and b.

The zebra frame allows the present system to determine a resolution ofthe embedded picture, and to divide the image into “bold” or unknownpixels. As described earlier, the color value of some pixels may beunknown. “Bold” pixels allow the present system to determine the propercolor value of a pixel according to a voting process. In someembodiments, the voting process can choose the color value of the mostfrequent pixel close to the “bold” or unknown pixel to be the colorvalue of the “bold” pixel. Otherwise, if the result of the voting isunclear—for example, if there is no pixel with a frequency much largerthan the other pixels—the present system can mark the pixel as anerasure (i.e., missing). Therefore, using the two-level error correctiondescribed earlier, the pixel can be restored.

Image Fragmentation

FIG. 7 illustrates a block diagram for frame fragmentation fordistortion correction in accordance with certain embodiments of thepresent disclosure. The rectangular cells can be divided into sub-images704 using separating lines 702.

Frame fragmentation or image fragmentation refers to a complementarytechnique to distortion correction (shown in FIG. 6). In someembodiments, dividing the image into a set of sub-images 704 allows thepresent system to work with every sub-image 704, without requiring thedistortion compensation technique described earlier because sub-images704 are sufficiently small that there is not as much distortion as withthe entire encoded image. In some embodiments, sub-images 704 can beseparated with separating lines 702. In some embodiments, separatinglines 702 can be black lines, which can also became part of the zebraborder.

The present system can use the same encoding and decoding techniquesdescribed earlier (shown in FIGS. 3 and 4) for encoding and decoding ofsub-images 704. For example, the present system can leverage thetwo-level error correction, scrambler, etc. described earlier.

In some embodiments, the present system can skip the distortioncorrection described earlier (shown in FIG. 6). The present system canleverage the relatively smaller size of sub-images 704 to skipnon-linear distortion compensation of each sub-image 704. Accordingly,the present system can decode sub-images 704 with better accuracy.

Of course, although specific steps are disclosed in FIGS. 3 and 4, suchsteps are exemplary. That is, the present system is well-suited toperforming various other steps or variations of the steps recited inFIGS. 3 and 4. The steps in FIGS. 3 and 4 may be performed in an orderdifferent than presented, and that not all of the steps may beperformed. FIGS. 3 and 4 include processes that, in various embodiments,are carried out by a processor under the control of computer-readableand computer-executable instructions. Embodiments of the presentdisclosure may thus be stored as non-transitory computer-readable mediaor computer-executable instructions including, but not limited to,firmware updates, software update packages, or hardware (e.g., ROM).

Reference has been made in detail to various embodiments in accordancewith the present disclosure, examples of which are illustrated in theaccompanying drawings. While the invention has been described inconjunction with various embodiments, these various embodiments are notintended to limit the invention. On the contrary, the invention isintended to cover alternatives, modifications, and equivalents, whichmay be included within the scope of the invention as construed accordingto the appended claims. Furthermore, in the detailed description ofvarious embodiments, numerous specific details have been set forth inorder to provide a thorough understanding of the invention. However, theinvention may be practiced without these specific details. In otherinstances, well known methods, procedures, components, and circuits havenot been described in detail, so as not to unnecessarily obscure aspectsof the invention.

Some portions of the detailed descriptions have been presented in termsof procedures, logic blocks, processing, and other symbolicrepresentations of operations on data bits within a non-transitorycomputer memory. These descriptions and representations are the meansused by those skilled in the data processing arts to most effectivelyconvey the substance of their work to others skilled in the art. In thepresent disclosure, a procedure, logic block, process, or the like, isconceived to be a self-consistent sequence of operations or steps orinstructions leading to a desired result. The operations or steps arethose utilizing physical manipulations and transformations of physicalquantities. Usually, although not necessarily, these quantities take theform of electrical or magnetic signals capable of being stored,transferred, combined, compared, and otherwise manipulated in a computersystem or computing device. It has been convenient at times, principallyfor reasons of common usage, to refer to these signals as records,transactions, bits, values, elements, symbols, characters, samples,pixels, or the like.

Of course, all of these and similar terms are to be associated with theappropriate physical quantities and are merely convenient labels appliedto these quantities. Unless specifically stated otherwise as apparentfrom the foregoing discussions, it is appreciated that throughout thepresent disclosure, discussions utilizing terms such as “deactivating,”“disabling,” “re-activating,” “enabling,” “sending,” “receiving,”“determining,” “flushing,” “responding,” “generating,” “making,”“blocking,” “accessing,” “taking a snapshot,” “associating,” “allowing,”“updating,” or the like, refer to actions and processes of a computersystem or similar electronic computing device or processor. The computersystem or similar electronic computing device manipulates and transformsdata represented as physical (electronic) quantities within the systemmemories, registers or other such information storage, transmission ordisplay devices.

The present systems and methods can be implemented in a variety ofarchitectures and configurations. For example, the present systems andmethods can be implemented as part of a distributed computingenvironment, a cloud computing environment, a client server environment,etc. The embodiments described herein may be discussed in the generalcontext of computer-executable instructions residing on some form ofnon-transitory computer-readable storage medium, such as programmodules, executed by one or more computers, computing devices, or otherdevices. As described earlier, non-limiting examples ofcomputer-readable storage media may include storage media andcommunication media. Generally, program modules include routines,programs, objects, components, data structures, etc., that performparticular tasks or implement particular abstract data types. Thefunctionality of the program modules may be combined or distributed asdesired in various embodiments.

The foregoing descriptions of specific embodiments of the presentsystems and methods have been presented for purposes of illustration anddescription. The specific embodiments are not intended to be exhaustiveor to limit the invention to the precise forms disclosed, and manymodifications and variations are possible in light of the abovedescription. The embodiments were chosen and described in order to bestexplain the principles of the invention and its practical application,to thereby enable others skilled in the art to best utilize theinvention and various embodiments with various modifications as aresuited to the particular use contemplated. It is intended that the scopeof the invention be defined by the claims appended hereto and theirequivalents.

What is claimed is:
 1. A computer-implemented method of encoding datausing a color space, the method comprising: receiving, at a capturingdevice, data for encoding; determining, at the capturing device, a codesequence by performing error correction on the data; creating, at thecapturing device, colored cells to provide an encoded representation ofthe received data in an image by determining a color encoding for one ormore codes in the code sequence by mapping the code sequence to a streamof integers, each integer in the stream of integers corresponding to acolor in the color space for a cell in the image; partitioning, at thecapturing device, the colored cells into subimages of the image, thesubimages providing smaller areas for assisting in decoding the image;and creating, at the capturing device, one or more additional cells,including color calibration cells, adjoining the image, the one or moreadditional cells for assisting in decoding the image by determining apseudo-Euclidean metric in the color space, the metric measuringdistance including a measure of an average of: (a) a red component inthe colored cells and (b) a red component in the color calibrationcells.
 2. The method of claim 1, the one or more additional cellsincluding a frame surrounding the image.
 3. The method of claim 1,wherein the pseudo-Euclidean metric is defined as:${\Delta\; C} = \sqrt{{\left( {X + \frac{\overset{\_}{r}}{256}} \right)\;\Delta\; R^{2}} + {Y\;\Delta\; G^{2}} + {\left( {Z + \frac{255 - \overset{\_}{r}}{256}} \right)\Delta\; B^{2}}}$wherein: Δ C is a pseudo-Euclidean distance between two colors, C₁includes a red component R₁, a green component G₁, and a blue componentB₁, C₂ includes a red component R₂, a green component G₂, and a bluecomponent B₂, X, Y and Z represent coefficients, ΔR=R₂−R₁, ΔG=G₂−G₁,ΔB=B₂−B₁, and $\overset{\_}{r} = {\frac{R_{1} + R_{2}}{2}.}$
 4. Themethod of claim 3, wherein the coefficients of the pseudo-Euclideanmetric are defined as X=2, Y=4 and Z=2.
 5. A computer-implemented methodof decoding an image using a color space, the method comprising:receiving, at a processing device including a decoding module, an imageto decode; locating, at the decoding module, the image by locating oneor more additional cells, including color calibration cells, adjoiningthe image; locating, at the decoding module, one or more subimages ofthe image; and for each subimage, decoding, at the decoding module, databased at least in part on colored cells in the subimage and the one ormore additional cells adjoining the image by determining apseudo-Euclidean metric in the color space, the metric measuringdistance including a measure of an average of: (a) a red component inthe colored cells and (b) a red component in the color calibrationcells.
 6. The method of claim 5, the one or more additional cellsincluding a frame surrounding the image.
 7. The method of claim 5,wherein the pseudo-Euclidean metric is defined as:${\Delta\; C} = \sqrt{{\left( {X + \frac{\overset{\_}{r}}{256}} \right)\;\Delta\; R^{2}} + {Y\;\Delta\; G^{2}} + {\left( {Z + \frac{255 - \overset{\_}{r}}{256}} \right)\Delta\; B^{2}}}$wherein: Δ C is a pseudo-Euclidean distance between two colors, C₁includes a red component R₁, a green component G₁, and a blue componentB₁, C₂ includes a red component R₂, a green component G₂, and a bluecomponent B₂, X, Y and Z represent coefficients, ΔR=R₂−R₁, ΔG=G₂−G₁,ΔB=B₂−B₁, and $\overset{\_}{r} = {\frac{R_{1} + R_{2}}{2}.}$
 8. Themethod of claim 7, wherein the coefficients of the pseudo-Euclideanmetric are defined as X=2, Y=4 and Z=2.
 9. A system for encoding datausing a color space, the system comprising: memory; storage; and atleast one processor configured to use the memory and storage to: receivedata for encoding; determine a code sequence by performing errorcorrection on the data; create colored cells to provide an encodedrepresentation of the received data in an image by determining a colorencoding for one or more codes in the code sequence by mapping the codesequence to a stream of integers, each integer in the stream of integerscorresponding to a color in the color space for a cell in the image;partition the colored cells into subimages of the image, the subimagesproviding smaller areas for assisting in decoding the image; and createone or more additional cells, including color calibration cells,adjoining the image, the one or more additional cells for assisting indecoding the image by determining a pseudo-Euclidean metric in the colorspace, the metric measuring distance including a measure of an averageof: (a) a red component in the colored cells and (b) a red component inthe color calibration cells.
 10. The system of claim 9, the one or moreadditional cells including a frame surrounding the image.
 11. The systemof claim 9, wherein the pseudo-Euclidean metric is defined as:${\Delta\; C} = \sqrt{{\left( {X + \frac{\overset{\_}{r}}{256}} \right)\;\Delta\; R^{2}} + {Y\;\Delta\; G^{2}} + {\left( {Z + \frac{255 - \overset{\_}{r}}{256}} \right)\Delta\; B^{2}}}$wherein: Δ C is a pseudo-Euclidean distance between two colors, C₁includes a red component R₁, a green component G₁, and a blue componentB₁, C₂ includes a red component R₂, a green component G₂, and a bluecomponent B₂, X, Y and Z represent coefficients, ΔR=R₂−R₁, ΔG=G₂−G₁,ΔB=B₂−B₁, and $\overset{\_}{r} = {\frac{R_{1} + R_{2}}{2}.}$
 12. Thesystem of claim 11, wherein the coefficients of the pseudo-Euclideanmetric are defined as X=2, Y=4 and Z=2.
 13. A system for decoding animage using a color space, the system comprising: memory; storage; andat least one processor configured to: receive an image to decode; locatethe image by locating one or more additional cells, including colorcalibration cells, adjoining the image; locate one or more subimages ofthe image; and for each subimage, decode data based at least in part oncolored cells in the subimage and the one or more additional cellsadjoining the image by determining a pseudo-Euclidean metric in thecolor space, the metric measuring distance including a measure of anaverage of: (a) a red component in the colored cells and (b) a redcomponent in the color calibration cells.
 14. The system of claim 13,the one or more additional cells including a frame surrounding theimage.
 15. The system of claim 13, wherein the pseudo-Euclidean metricis defined as:${\Delta\; C} = \sqrt{{\left( {X + \frac{\overset{\_}{r}}{256}} \right)\;\Delta\; R^{2}} + {Y\;\Delta\; G^{2}} + {\left( {Z + \frac{255 - \overset{\_}{r}}{256}} \right)\Delta\; B^{2}}}$wherein: Δ C is a pseudo-Euclidean distance between two colors, C₁includes a red component R₁, a green component G₁, and a blue componentB₁, C₂ includes a red component R₂, a green component G₂, and a bluecomponent B₂, X, Y and Z represent coefficients, ΔR=R₂−R₁, ΔG=G₂−G₁,ΔB=B₂−B₁, and $\overset{\_}{r} = {\frac{R_{1} + R_{2}}{2}.}$
 16. Thesystem of claim 15, wherein the coefficients of the pseudo-Euclideanmetric are defined as X=2, Y=4 and Z=2.